Over the past few weeks, I’ve been thinking a lot about how modern operations teams manage growing infrastructure complexity across cloud environments.
As organizations scale across AWS, Azure, SaaS platforms, APIs, remote teams, and distributed systems, operational support becomes much more than simply “handling tickets.”
It becomes a challenge of maintaining operational continuity, visibility, automation, and fast incident response at scale.
To explore that challenge, I built SenatSupport AI — an AI-assisted IT operations platform designed to simulate how modern cloud and support operations teams can automate incident management workflows using serverless architecture, infrastructure automation, and AI-assisted operational logic.
Instead of focusing only on infrastructure deployment, I wanted to build something closer to how real operational systems behave:
- intelligent ticket processing
- automated urgency scoring
- engineer assignment workflows
- operational dashboards
- incident escalation
- monitoring and alerting
- Infrastructure as Code deployment workflows
The platform was fully deployed on AWS using serverless and infrastructure automation principles, while intentionally keeping a multi-cloud operational mindset where systems increasingly span AWS, Azure, APIs, SaaS platforms, and distributed infrastructure environments.
Core architecture components include:
- Amazon API Gateway
- AWS Lambda
- Amazon DynamoDB
- Amazon Bedrock
- Amazon SNS
- Amazon CloudWatch
- Amazon ECR
- Terraform
- Docker
- GitHub Actions
- Amazon S3 Static Website Hosting
One architectural decision I found especially valuable was separating storage responsibilities using two Amazon S3 buckets:
- one bucket dedicated to hosting the frontend application
- another bucket dedicated to Terraform remote state management for infrastructure consistency and reproducible deployments
That separation helped simulate how production engineering environments typically isolate operational responsibilities across infrastructure layers.
One of the biggest lessons from this project was realizing how much operational complexity exists beyond simply deploying cloud resources.
Building the platform involved debugging API communication issues, managing infrastructure consistency, handling deployment drift, configuring monitoring and alerting workflows, troubleshooting serverless integrations, and maintaining operational visibility across multiple services.
That experience reinforced something I’ve been finding increasingly important in cloud engineering:
The future is not only infrastructure deployment —
it’s operational automation, intelligent systems management, observability, and scalable platform engineering.
👇 Architecture overview below
One of the goals behind SenatSupport AI was to create visibility for multiple operational perspectives inside the same system.
The platform includes dashboards designed for both engineering workflows and leadership visibility.
From the engineering side, tickets can move through operational lifecycle stages such as:
- Open
- In Progress
- Resolved
- Closed
Engineers can update ticket status, manage incidents, and track operational workflows directly through the platform.
At the leadership level, the Director Dashboard provides real time operational metrics such as:
- total incidents
- resolved tickets
- open operational issues
- critical incidents requiring escalation
This helps simulate how modern operations teams monitor organizational health and incident response efficiency at scale.
Another important part of the project was operational alerting and monitoring.
The platform includes automated notification workflows using Amazon SNS and operational monitoring using Amazon CloudWatch.
When high priority incidents are detected, automated alert emails are triggered to simulate real world escalation workflows used by operations and support teams.
I also implemented monitoring logic designed to detect repeated Lambda execution failures and trigger operational alarms automatically.
This introduced another important layer of operational visibility beyond basic infrastructure deployment and helped reinforce how monitoring becomes critical in distributed cloud systems.
To improve deployment consistency and reduce manual infrastructure management, the entire environment was provisioned using Terraform and Infrastructure as Code principles.
The deployment workflow included:
- serverless infrastructure provisioning
- API integrations
- Lambda deployments
- container image management using Amazon ECR
- Docker-based packaging workflows
- remote Terraform state management
- CI/CD automation using GitHub Actions
One thing this project reinforced for me is how quickly operational complexity grows once multiple services, APIs, monitoring systems, deployment workflows, and automation layers begin interacting together.
That complexity is exactly what makes cloud engineering, DevOps, and platform engineering so interesting to me.
Below are the project repository and live deployment used for this implementation.
GitHub Repository:
SenatSupport AI:
https://github.com/wilfriedbako/project-senatsupport-AI
Live Demo:
http://senatsupport-frontend-bako.s3-website-us-east-1.amazonaws.com
Open to connecting with engineers and cloud professionals working across AWS, DevOps, platform engineering, and AI-assisted operational systems.
Thanks for reading — always open to feedback, architecture discussions, and connecting with other engineers building modern cloud and operational platforms.
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